Riduzione della dimensionalità (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Riduzione della dimensionalità" in Italian language version.

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14doi.org

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archive.org

E. Bingham e H. Mannila, Random projection in dimensionality reduction, in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining – KDD '01, 2001, pp. 245, DOI:10.1145/502512.502546, ISBN 978-1581133912.

arxiv.org

Blanton, K-corrections and filter transformations in the ultraviolet, optical, and near infrared, in The Astronomical Journal, vol. 133, n. 2, 2007, pp. 734–754, DOI:10.1086/510127, arXiv:astro-ph/0606170.
Ren, Non-negative Matrix Factorization: Robust Extraction of Extended Structures, in The Astrophysical Journal, vol. 852, n. 2, 2018, pp. 104, DOI:10.3847/1538-4357/aaa1f2, arXiv:1712.10317.
(EN) Guangtun B. Zhu, Nonnegative Matrix Factorization (NMF) with Heteroscedastic Uncertainties and Missing data, 19 dicembre 2016.
Ren, Using Data Imputation for Signal Separation in High Contrast Imaging, in The Astrophysical Journal, vol. 892, n. 2, 2020, pp. 74, DOI:10.3847/1538-4357/ab7024, arXiv:2001.00563.

binghamton.edu

ws2.binghamton.edu

Hongbing Hu, Stephen A. Zahorian, (2010) "Dimensionality Reduction Methods for HMM Phonetic Recognition", ICASSP 2010, Dallas, TX

columbia.edu

cs.columbia.edu

B. Shaw e T. Jebara, Structure preserving embedding (PDF), in Proceedings of the 26th Annual International Conference on Machine Learning – ICML '09, 2009, p. 1, DOI:10.1145/1553374.1553494, ISBN 9781605585161.

cujae.edu.cu

rielac.cujae.edu.cu

Reducing Vector Space Dimensionality in Automatic Classification for Authorship Attribution, in Revista Ingeniería Electrónica, Automática y Comunicaciones, vol. 38, n. 3, 2017, pp. 26–35, ISSN 1815-5928 (WC · ACNP).

doi.org

dx.doi.org

P. Pudil e J. Novovičová, Novel Methods for Feature Subset Selection with Respect to Problem Knowledge, in Liu (a cura di), Feature Extraction, Construction and Selection, 1998, pp. 101, DOI:10.1007/978-1-4615-5725-8_7, ISBN 978-1-4613-7622-4.
A Survey of Multilinear Subspace Learning for Tensor Data (PDF), in Pattern Recognition, vol. 44, n. 7, 2011, pp. 1540–1551, DOI:10.1016/j.patcog.2011.01.004.
Daniel D. Lee, Learning the parts of objects by non-negative matrix factorization, in Nature, vol. 401, n. 6755, 1999, pp. 788–791, DOI:10.1038/44565, PMID 10548103.
Blanton, K-corrections and filter transformations in the ultraviolet, optical, and near infrared, in The Astronomical Journal, vol. 133, n. 2, 2007, pp. 734–754, DOI:10.1086/510127, arXiv:astro-ph/0606170.
Ren, Non-negative Matrix Factorization: Robust Extraction of Extended Structures, in The Astrophysical Journal, vol. 852, n. 2, 2018, pp. 104, DOI:10.3847/1538-4357/aaa1f2, arXiv:1712.10317.
Ren, Using Data Imputation for Signal Separation in High Contrast Imaging, in The Astrophysical Journal, vol. 892, n. 2, 2020, pp. 74, DOI:10.3847/1538-4357/ab7024, arXiv:2001.00563.
Sam T. Roweis e Lawrence K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, in Science, vol. 290, n. 5500, 22 dicembre 2000, pp. 2323-2326, DOI:10.1126/science.290.5500.2323, PMID 11125150.
Zhenyue Zhang e Hongyuan Zha, Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment, in SIAM J. Sci. Comput., vol. 26, n. 1, 2004, pp. 313–338, DOI:10.1137/s1064827502419154.
(EN) Yoshua Bengio, Martin Monperrus e Hugo Larochelle, Nonlocal Estimation of Manifold Structure, in MIT Press, vol. 18, n. 10, 2006, pp. 2509-2528, DOI:10.1162/neco.2006.18.10.2509, PMID 16907635.
G. Baudat e F. Anouar, Generalized Discriminant Analysis Using a Kernel Approach, vol. 12, 2000, DOI:10.1162/089976600300014980, PMID 11032039.
CloudID: Trustworthy cloud-based and cross-enterprise biometric identification, in Expert Systems with Applications, vol. 42, n. 21, 30 novembre 2015, pp. 7905-7916, DOI:10.1016/j.eswa.2015.06.025.
(EN) Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection, in Similarity Search and Applications, Lecture Notes in Computer Science, vol. 10609, Springer International Publishing, 2017, pp. 188–203, DOI:10.1007/978-3-319-68474-1_13, ISBN 978-3-319-68474-1.
B. Shaw e T. Jebara, Structure preserving embedding (PDF), in Proceedings of the 26th Annual International Conference on Machine Learning – ICML '09, 2009, p. 1, DOI:10.1145/1553374.1553494, ISBN 9781605585161.
E. Bingham e H. Mannila, Random projection in dimensionality reduction, in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining – KDD '01, 2001, pp. 245, DOI:10.1145/502512.502546, ISBN 978-1581133912.

escholarship.org

C. Ding, X. He, H. Zha, H.D. Simon, Adaptive Dimension Reduction for Clustering High Dimensional Data, Proceedings of International Conference on Data Mining, 2002

loria.fr

members.loria.fr

Dimensionality Reduction: A Comparative Review (PDF), in J Mach Learn Res, vol. 10, 26 ottobre 2009, pp. 66–71.

neurips.cc

proceedings.neurips.cc

Algorithms for Non-negative Matrix Factorization (PDF), Advances in Neural Information Processing Systems, 2001.

nih.gov

ncbi.nlm.nih.gov

Daniel D. Lee, Learning the parts of objects by non-negative matrix factorization, in Nature, vol. 401, n. 6755, 1999, pp. 788–791, DOI:10.1038/44565, PMID 10548103.
Sam T. Roweis e Lawrence K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, in Science, vol. 290, n. 5500, 22 dicembre 2000, pp. 2323-2326, DOI:10.1126/science.290.5500.2323, PMID 11125150.
(EN) Yoshua Bengio, Martin Monperrus e Hugo Larochelle, Nonlocal Estimation of Manifold Structure, in MIT Press, vol. 18, n. 10, 2006, pp. 2509-2528, DOI:10.1162/neco.2006.18.10.2509, PMID 16907635.
G. Baudat e F. Anouar, Generalized Discriminant Analysis Using a Kernel Approach, vol. 12, 2000, DOI:10.1162/089976600300014980, PMID 11032039.

oadoi.org

Daniel D. Lee, Learning the parts of objects by non-negative matrix factorization, in Nature, vol. 401, n. 6755, 1999, pp. 788–791, DOI:10.1038/44565, PMID 10548103.
Blanton, K-corrections and filter transformations in the ultraviolet, optical, and near infrared, in The Astronomical Journal, vol. 133, n. 2, 2007, pp. 734–754, DOI:10.1086/510127, arXiv:astro-ph/0606170.
Ren, Non-negative Matrix Factorization: Robust Extraction of Extended Structures, in The Astrophysical Journal, vol. 852, n. 2, 2018, pp. 104, DOI:10.3847/1538-4357/aaa1f2, arXiv:1712.10317.
Ren, Using Data Imputation for Signal Separation in High Contrast Imaging, in The Astrophysical Journal, vol. 892, n. 2, 2020, pp. 74, DOI:10.3847/1538-4357/ab7024, arXiv:2001.00563.
Sam T. Roweis e Lawrence K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, in Science, vol. 290, n. 5500, 22 dicembre 2000, pp. 2323-2326, DOI:10.1126/science.290.5500.2323, PMID 11125150.
Zhenyue Zhang e Hongyuan Zha, Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment, in SIAM J. Sci. Comput., vol. 26, n. 1, 2004, pp. 313–338, DOI:10.1137/s1064827502419154.
(EN) Yoshua Bengio, Martin Monperrus e Hugo Larochelle, Nonlocal Estimation of Manifold Structure, in MIT Press, vol. 18, n. 10, 2006, pp. 2509-2528, DOI:10.1162/neco.2006.18.10.2509, PMID 16907635.
G. Baudat e F. Anouar, Generalized Discriminant Analysis Using a Kernel Approach, vol. 12, 2000, DOI:10.1162/089976600300014980, PMID 11032039.
CloudID: Trustworthy cloud-based and cross-enterprise biometric identification, in Expert Systems with Applications, vol. 42, n. 21, 30 novembre 2015, pp. 7905-7916, DOI:10.1016/j.eswa.2015.06.025.

psu.edu

citeseerx.ist.psu.edu

Kevin Beyer, Jonathan Goldstein, Raghu Ramakrishnan, Uri Shaft (1999) "When is "nearest neighbor" meaningful?". Database Theory—ICDT99, 217–235

springer.com

link.springer.com

(EN) Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection, in Similarity Search and Applications, Lecture Notes in Computer Science, vol. 10609, Springer International Publishing, 2017, pp. 188–203, DOI:10.1007/978-3-319-68474-1_13, ISBN 978-3-319-68474-1.

unibo.it

acnpsearch.unibo.it

Reducing Vector Space Dimensionality in Automatic Classification for Authorship Attribution, in Revista Ingeniería Electrónica, Automática y Comunicaciones, vol. 38, n. 3, 2017, pp. 26–35, ISSN 1815-5928 (WC · ACNP).

utoronto.ca

dsp.utoronto.ca

A Survey of Multilinear Subspace Learning for Tensor Data (PDF), in Pattern Recognition, vol. 44, n. 7, 2011, pp. 1540–1551, DOI:10.1016/j.patcog.2011.01.004.

worldcat.org

Reducing Vector Space Dimensionality in Automatic Classification for Authorship Attribution, in Revista Ingeniería Electrónica, Automática y Comunicaciones, vol. 38, n. 3, 2017, pp. 26–35, ISSN 1815-5928 (WC · ACNP).